Using MPI: portable parallel programming with the message-passing interface
Using MPI: portable parallel programming with the message-passing interface
Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Matrix Inversion Using Parallel Processing
Journal of the ACM (JACM)
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Combining Statistical and Relational Methods for Learning in Hypertext Domains
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
A cellular computer to implement the kalman filter algorithm
A cellular computer to implement the kalman filter algorithm
Classification of Text Documents
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Why collective inference improves relational classification
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Semi-supervised learning with graphs
Semi-supervised learning with graphs
Large-scale text categorization by batch mode active learning
Proceedings of the 15th international conference on World Wide Web
Batch mode active learning and its application to medical image classification
ICML '06 Proceedings of the 23rd international conference on Machine learning
Discriminative unsupervised learning of structured predictors
ICML '06 Proceedings of the 23rd international conference on Machine learning
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
Exploiting Network Structure for Active Inference in Collective Classification
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Personalized active learning for collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Effective label acquisition for collective classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Importance weighted active learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Combining link and content for community detection: a discriminative approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Batch Mode Active Learning with Applications to Text Categorization and Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Improving learning in networked data by combining explicit and mined links
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Reflect and correct: A misclassification prediction approach to active inference
ACM Transactions on Knowledge Discovery from Data (TKDD)
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Greedy is not Enough: An Efficient Batch Mode Active Learning Algorithm
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
A large-scale active learning system for topical categorization on the web
Proceedings of the 19th international conference on World wide web
Social action tracking via noise tolerant time-varying factor graphs
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Batch Mode Sparse Active Learning
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
On the Foundations of Relaxation Labeling Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Learning continuous time bayesian networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
Active learning for networked data based on non-progressive diffusion model
Proceedings of the 7th ACM international conference on Web search and data mining
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We study a novel problem of batch mode active learning for networked data. In this problem, data instances are connected with links and their labels are correlated with each other, and the goal of batch mode active learning is to exploit the link-based dependencies and node-specific content information to actively select a batch of instances to query the user for learning an accurate model to label unknown instances in the network. We present three criteria (i.e., minimum redundancy, maximum uncertainty, and maximum impact) to quantify the informativeness of a set of instances, and formalize the batch mode active learning problem as selecting a set of instances by maximizing an objective function which combines both link and content information. As solving the objective function is NP-hard, we present an efficient algorithm to optimize the objective function with a bounded approximation rate. To scale to real large networks, we develop a parallel implementation of the algorithm. Experimental results on both synthetic datasets and real-world datasets demonstrate the effectiveness and efficiency of our approach.